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pyTWMR: transcriptome-wide Mendelian randomization in python.
Oreshkov, Sergey; Lepik, Kaido; Santoni, Federico.
Affiliation
  • Oreshkov S; Endocrine, Diabetes and Metabolism Service, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1005, Switzerland.
  • Lepik K; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland.
  • Santoni F; Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.
Bioinformatics ; 40(8)2024 08 02.
Article in En | MEDLINE | ID: mdl-39128017
ABSTRACT
MOTIVATION Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies.

RESULTS:

Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants. AVAILABILITY AND IMPLEMENTATION pyTWMR is available at github.com/soreshkov/pyTWMR.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Polymorphism, Single Nucleotide / Mendelian Randomization Analysis / Transcriptome Limits: Humans Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2024 Document type: Article Affiliation country: Switzerland Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Polymorphism, Single Nucleotide / Mendelian Randomization Analysis / Transcriptome Limits: Humans Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2024 Document type: Article Affiliation country: Switzerland Country of publication: United kingdom